First, we create a dataset containing element network information joined with other attributes of elements. We specifically only consider elements that are known to form at least five minerals on Earth since 4.33 Ga. The following elements are therefore excluded (in parentheses is given the number of minerals it forms): Dy (1), Er (1), Gd (1), Hf (1), Sm (1), Re (2), Rb (3).

## # A tibble: 66 × 16
##    element n_elements n_minerals n_localities element_name element_hsab
##    <chr>        <dbl>      <dbl>        <dbl> <chr>        <chr>       
##  1 Ag              28        172         3166 Silver       Soft acid   
##  2 Al              52        881        10322 Aluminum     Hard acid   
##  3 As              59        634         4664 Arsenic      Hard acid   
##  4 Au              12         31         5616 Gold         Soft acid   
##  5 B               42        232         1295 Boron        Soft acid   
##  6 Ba              40        218         2904 Barium       Hard acid   
##  7 Be              36        121         1352 Beryllium    Hard acid   
##  8 Bi              40        212         1821 Bismuth      Int. acid   
##  9 Br              15         13           88 Bromine      Soft base   
## 10 C               50        417         9650 Carbon       Soft base   
## # … with 56 more rows, and 10 more variables: atomic_mass <dbl>,
## #   number_of_protons <dbl>, element_table_period <dbl>,
## #   element_table_group <dbl>, atomic_radius <dbl>, pauling <dbl>,
## #   element_metal_type <chr>, element_density <dbl>,
## #   element_specific_heat <dbl>, element_crust_percent_weight <dbl>

Analysis 1: What is the relationship between number of elements interacted with and number of minerals formed?


First, we explore whether we should likely transform an axis on a log-scale. Model diagnostics are shown below for the model with log y and untransformed x, whose diagnostics best meet assumptions.



The resulting model is as follows - THERE IS A POSITIVE RELATIONSHIP.

Analysis 2: What is the relationship between number of elements interacted with and number of localities it is found at?


First, we explore whether we should likely transform an axis on a log-scale. Model diagnostics are shown below for the model with log y and untransformed x, whose diagnostics best meet assumptions.



The resulting model is as follows - THERE IS A POSITIVE RELATIONSHIP.

Analysis 3: What is the relationship between number of elements interacted with and percentage of crust?

Note that there are six elements which do not appear in this analysis because they are missing crust data - C, H, N, REE (rare earth elements), Rh, Te.


First, we explore whether we should likely transform an axis on a log-scale. Model diagnostics are shown below for the model with log x and untransformed y, whose diagnostics best meet assumptions.




The resulting model is as follows - THERE IS A POSITIVE RELATIONSHIP.

Analysis 4: What is the relationship between number of elements interacted with and electronegativity?


First, we explore whether we should likely transform an axis on a log-scale. Model diagnostics are shown below for the model with both untransformed y and x, whose diagnostics best meet assumptions.




The resulting model is as follows - THERE IS NO RELATIONSHIP.:

Analysis 5: What is the relationship between number of minerals formed and electronegativity?


First, we explore whether we should likely transform an axis on a log-scale. Model diagnostics are shown below for the model with log y and untransformed x, whose diagnostics best meet assumptions.




The resulting model is as follows - THERE IS NO RELATIONSHIP.

Analysis 6: Can the atomic number (number of protons) explain the number of elements interacted with?


First, we explore whether we should likely transform an axis on a log-scale. Model diagnostics are shown below for the model with both axes untransformed; all diagnostics are about the same, so we’ll use the regular data.




The resulting model is as follows - THERE IS A NEGATIVE RELATIONSHIP.